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Recognition by probabilistic hypothesis construction. (English) Zbl 1098.68822

Pajdla, Tomáš (ed.) et al., Computer vision – ECCV 2004. 8th European conference on computer vision, Prague, Czech Republic, May 11–14, 2004. Proceedings, Part I. Berlin: Springer (ISBN 3-540-21984-6/pbk). Lecture Notes in Computer Science 3021, 55-68 (2004).
Summary: We present a probabilistic framework for recognizing objects in images of cluttered scenes. Hundreds of objects may be considered and searched in parallel. Each object is learned from a single training image and modeled by the visual appearance of a set of features, and their position with respect to a common reference frame. The recognition process computes identity and position of objects in the scene by finding the best interpretation of the scene in terms of learned objects. Features detected in an input image are either paired with database features, or marked as clutters. Each hypothesis is scored using a generative model of the image which is defined using the learned objects and a model for clutter. While the space of possible hypotheses is enormously large, one may find the best hypothesis efficiently – we explore some heuristics to do so. Our algorithm compares favorably with state-of-the-art recognition systems.
For the entire collection see [Zbl 1094.68003].

MSC:

68T45 Machine vision and scene understanding
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